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Deadline Aware Energy-Efficient Task Scheduling Model for a Virtualized Server

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Abstract

The demand of cloud-based services is increasing day by day. To fulfill the seamless computing demand of consumers, a large number of servers are installed in the datacenters. These servers consume high energy. Reducing the energy consumption of servers with the same performance of service is a vital challenge. Energy-efficient task scheduling is one of the method to reduce energy consumption while preserving the task constraints. This paper presents a deadline and energy-aware scheduling (DEAS) model for a virtualized server to achieve energy efficiency while executing deadline conscious tasks. These tasks are independent in nature and arrives dynamically. The presented DEAS model follows the heuristic approach where the first instance guarantee ratio (GR) is maximized to raise the energy efficiency of the server per unit of work performed. Task slack time is utilized in an innovative way to improve the GR of the server. In this way, the average energy consumption (energy consumption per task) of the server per unit of work done is minimized. The energy efficiency of the server in its idle state is further achieved by applying core-level granularity of dynamic voltage and frequency scaling (DVFS) technology. The DEAS model is evaluated through extensive simulation experiments using the CloudSim simulator. Results show that DEAS model performs better than existing models on the account of considered performance metrics, i.e., GR, total energy consumption, energy consumption per task, and resource utilization.

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References

  1. Nayyar A. Private virtual infrastructure (pvi) model for cloud computing. Int J Softw Eng Res Pract. 2011;1(1):10–4.

    Google Scholar 

  2. Kaur A, Gupta P, Singh M, Nayyar A. Data placement in era of cloud computing: a survey, taxonomy and open research issues. Scal Comput Pract Exp. 2019;20(2):377–98.

    Google Scholar 

  3. Bianchini R, Rajamony R. Power and energy management for server systems. Computer. 2004;37(11):68–76.

    Article  Google Scholar 

  4. Xiaodan Z, Gong L, and Li J. Research on green computing evaluation system and method. In: 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA). 2012. https://doi.org/10.1109/iciea.2012.6360902.

  5. Intel processor, http://www.intel.in/content/www/in/en/processors/xeon/xeon-processor-e7-family.html. Accessed 4 May 2020

  6. Amd processor, http://products.amd.com/en-us/search/cpu/amd-opteron%E2%84%A2/amd-opteron%E2%84%A2-6300-series-processor. Accessed 4 May 2020

  7. Dynamic Power Management Techniques for Embedded Systems, http://cseweb.ucsd.edu/~gdhiman/Gaurav_files/CSE-237A/TopicResearch/DPMPolicies.htm. Accessed 4 May 2020

  8. Al-Tarawneh M, Al Tarawneh ZA, Alnawayseh SE. A CPU-guided dynamic voltage and frequency scaling (DVFS) of off-chip buses in homogenous Multicore processors. Int Rev Comput Softw (IRECOS). 2015;10(7):735–47.

    Article  Google Scholar 

  9. Garg N, Singh D, Goraya MS. Energy aware hardware and software approaches in cloud environment. Int J Comput Sci Commun Netw. 2017;7(3):66–9.

    Google Scholar 

  10. Tomas L, Tordsson J. Improving cloud infrastructure utilization through overbooking. In: Proceedings of the 2013 ACM Cloud and Autonomic Computing Conference on - CAC '13. 2013; https://doi.org/10.1145/2494621.2494627.

  11. Thomas E, Mahmood Z, Puttini R. Cloud computing: Concepts, Technology, and Architecture. Prentice Hall. 2013.

  12. Server Virtualization, https://www.energystar.gov/products/low_carbon_it_campaign/12_ways_save_energy_data_center/server_virtualization. Accessed 4 May 2020

  13. Ding Y, Qin X, Liu L, Wang T. Energy efficient scheduling of virtual machines in cloud with deadline constraint. Fut Gen Comput Syst. 2015;50:62–74.

    Article  Google Scholar 

  14. Vincent N, Goossens J, Devillers R, Milojevic D, Navet N. Power-aware real-time scheduling upon identical multiprocessor platforms. In: 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008). 2008. https://doi.org/10.1109/sutc.2008.31.

  15. Kherani FF, Vania J. Load balancing in cloud computing. Int J Eng Dev Res. 2014;2(1):907–12.

    Google Scholar 

  16. Chen DR, Chen YS, Lai MF. A practical slack-time analysis method for DVS real-time scheduling. In:18th International Conference on Real-Time and Network Systems, 2010; 139–148. hal-00546926.

  17. Dakai Z, Melhem R, Childers BR. Scheduling with dynamic voltage/speed adjustment using slack reclamation in multiprocessor real-time systems. IEEE Trans Parallel Distrib Syst. 2003;14(7):686–700.

    Article  Google Scholar 

  18. Ejlali A, Bashir MAH, Petru E. Low-energy standby-sparing for hard real-time systems. IEEE Trans Comput Aid Design Integ Circ Syst. 2012; 31(3): 329–342.

  19. Fly-by-wire, http://www.tejas.gov.in/technology/fly_by_wire.html. Accessed 2 May 2020

  20. Wang L, Khan SU, Chen D, Kołodziej J, Ranjan R, Xu CZ, and Zomaya A. Energy-aware parallel task scheduling in a cluster. Fut Gen Comput Syst. 2013; 29(7): 1661–1670.

  21. Haque MA, Aydin H, and Zhu D. Energy-aware standby-sparing technique for periodic real-time applications. In: 2011 IEEE 29th International Conference on Computer Design (ICCD). 2011; https://doi.org/10.1109/iccd.2011.6081396.

  22. Reddy SP, Chandan HK. Energy aware scheduling of real-time and non-real-time tasks on servers: (Extensible to embedded systems). In: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), 2014; https://doi.org/10.1109/icgccee.2014.6922289.

  23. Reddy SP, Chandan HK. Energy aware scheduling of real-time and non-real-time tasks on cloud processors (Green Cloud Computing). In: International Conference on Information Communication and Embedded Systems (ICICES2014), 2014; https://doi.org/10.1109/icices.2014.7033827.

  24. Zhu X, He C, Li K, Qin X. Adaptive energy-efficient scheduling for real-time tasks on DVS-enabled heterogeneous clusters. J Parallel Distrib Comput. 2012;72(6):751–63.

    Article  Google Scholar 

  25. Attia KM, Mostafa AEH, Ali HA. Dynamic power management techniques in multi-core architectures: a survey study. Ain Shams Eng J. 2017;8(3):445–56.

    Article  Google Scholar 

  26. Isci C, Buyuktosunoglu A, Cher CY, Bose P, Martonosi M. An analysis of efficient multi-core global power management policies: maximizing performance for a given power budget. In: 2006 39th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO'06). 2006; https://doi.org/10.1109/micro.2006.8.

  27. Wonyoung K, Gupta MS, Wei GY, Brooks D. System level analysis of fast, per-core DVFS using on-chip switching regulators.In: 2008 IEEE 14th International Symposium on High Performance Computer Architecture. 2008; https://doi.org/10.1109/hpca.2008.4658633.

  28. Bergamaschi R, Han G, Buyuktosunoglu A, Patel H, Nair I, Dittmann G, et al. Exploring power management in multi-core systems. In: 2008 Asia and South Pacific Design Automation Conference. 2008; https://doi.org/10.1109/aspdac.2008.4484043

  29. Misbha DS, Jeba JR. Scheduling effective cloud updates in streaming data warehouses using RECSS algorithm. Int J Appl Eng Res. 2016;11(5):3632–6.

    Google Scholar 

  30. Davenport AJ, Gefflot C, Beck JC. Slack-based technique for robust schedules. In: Proceedings of the sixth European Conference on Planning. 2014; 43–49.

  31. Zhu X, Yang LT, Chen H, Wang J, Yin S, Liu X. Real-time tasks oriented energy-aware scheduling in virtualized clouds. IEEE Trans Cloud Comput. 2014; 2(2): 168–180.

  32. Venu, B. ArXiv.org E-Print Archive. https://arxiv.org/ftp/arxiv/papers/1110/1110.3535.pdf. Accessed 14 May 2020.

  33. Calheiros RN, Ranjan R, Beloglazov A, Rose CAD, and Buyya RK. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp. 2010; 41(1): 23–50.

  34. Shen H, Liu G. An efficient and trustworthy resource sharing platform for collaborative cloud computing. IEEE Trans Parallel Distrib Syst. 2014;25(4):862–75.

    Article  Google Scholar 

  35. Garg N, Goraya MS. Task deadline-aware energy-efficient scheduling model for a virtualized cloud. Arab J Sci Eng. 2018;43(2):829–41.

    Article  Google Scholar 

  36. Leh GS, Wu J, Shukla S, Farrens MK, Ghosal D. Model-driven joint optimization of power and latency guarantee in data center applications. SN Comput Sci. 2019; https://doi.org/10.1007/s42979-019-0030-z.

  37. Nayyar A. Handbook of cloud computing: basic to advance research on the concepts and design of cloud computing. BPB Publications. 2019.

  38. U.S. Energy Information Administration, http://www.eia.gov/electricity/monthly/pdf/epm.pdf. Accessed 2 May 2020

  39. Singh P, Gupta P, Jyoti K, Nayyar A. Research on auto-scaling of web applications in cloud: survey, trends and future directions. Scal Comput Pract Exp. 2019;20(2):399–432.

    Google Scholar 

  40. Server energy consumption, http://www.zdnet.com/article/toolkit-calculate-datacenter-server-power-usage/. Accessed 2 May 2020

  41. Singh SP, Nayyar A, Kaur H, Singla A. Dynamic task scheduling using balanced VM allocation policy for fog computing platforms. Scal Comput Pract Exp. 2019;20(2):433–56.

    Google Scholar 

  42. Zhang Y, Chen L, Shen H, Cheng X. An energy-efficient task scheduling heuristic algorithm without virtual machine migration in real-time cloud environments. Netw Syst Secur. 2016. https://doi.org/10.1007/978-3-319-46298-1_6.

    Article  Google Scholar 

  43. Yang J, Xu X, Tang W, Hu C, Dou W, Chen J. A task scheduling method for energy-performance trade-off in clouds. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2016; https://doi.org/10.1109/hpcc-smartcity-dss.2016.0146.

  44. Chen H, Liu G, Yin S, Liu X, Qiu D. ERECT: energy-efficient reactive scheduling for real-time tasks in heterogeneous virtualized clouds. J Comput Sci. 2018. https://doi.org/10.1016/j.jocs.2017.03.017.

    Article  Google Scholar 

  45. Terekhov D, Down DG, Beck JC. Queueing-theoretic approaches for dynamic scheduling: a survey. Surv Oper Res Manag Sci. 2014. https://doi.org/10.1016/j.sorms.2014.09.001.

    Article  MathSciNet  Google Scholar 

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Correspondence to Neha Garg.

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Garg, N., Singh, D. & Singh Goraya, M. Deadline Aware Energy-Efficient Task Scheduling Model for a Virtualized Server. SN COMPUT. SCI. 2, 169 (2021). https://doi.org/10.1007/s42979-021-00571-2

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